Experts in ICM: What's Next for AI in Incentive Compensation

In our Experts in ICM Series Q&A series, we’re sitting down with leaders from across the incentive compensation and sales performance management space to explore learnings, trends, and opportunities that exist for today’s ICM and SPM professionals.

In the spirit of learning (and, of course, sharing) more about how AI is impacting incentive compensation, sales performance, and operational efficiency, we recently sat down with CaptivateIQ’s Senior Vice President of Engineering Product & Design, Nahi Ojeil.

AI has generated a lot of hype over the past couple of years. Looking back, where did we think AI would be today, and how does reality compare to expectations?

Great question. Initially, there was a lot of concern around AI’s accuracy, particularly with hallucination—when AI generates incorrect responses. While AI has improved significantly in this regard, it’s still an issue in contexts where precision is critical. That said, one of the most exciting developments has been how much easier AI is to use. Early on, users had to phrase questions in very specific ways to get a useful response. Now, AI can interpret and respond to a broader range of queries much more effectively. We’ve also seen a shift from general AI applications back toward specialized AI use cases, particularly in sales performance management and incentive compensation.

What trends are you seeing in how AI is being applied in sales performance management and incentive compensation?

One of the biggest factors influencing AI adoption in this space is the need for precision. When you’re compensating people based on performance data, there’s zero tolerance for errors. This has meant that AI adoption in ICM (Incentive Compensation Management) and SPM (Sales Performance Management) has lagged behind areas where AI can afford to be less precise. That said, we’re starting to see AI make an impact in three core areas:

1. AI as a Copilot – AI is helping sales and compensation teams complete tasks more efficiently, such as managing administrative workflows, handling commission disputes, and optimizing compensation plan modeling.
2. AI as a Coach – AI is being used to provide guidance to sales reps and admins, helping them understand their plans, track performance, and identify opportunities to improve attainment.
3. AI as an Insight Engine – AI is starting to power deeper analytics, uncovering patterns in sales data and compensation performance that help businesses make more strategic decisions.

A recent survey showed that while AI adoption in sales performance management is growing rapidly, only about 20% of companies are actively using it today. What are the biggest barriers to adoption?

There are a few key blockers. The first is organizational culture—if leadership isn’t fully bought in on AI, it can be difficult to secure budget and resources to implement it. Another major barrier is integration. Many companies struggle to embed AI into their existing workflows without disrupting operations. Change management is always a challenge, especially in fields that deal with financial accuracy and compliance. Lastly, security concerns around sensitive compensation data are preventing some organizations from fully embracing AI-driven automation.

Have you seen any companies successfully overcome these barriers? Any best practices?

Absolutely. The most successful companies start by identifying a clear problem AI can solve rather than trying to adopt AI for the sake of it. When leadership understands the impact AI can have on efficiency and performance, it becomes much easier to secure buy-in. Another key best practice is starting small—companies that roll out AI incrementally, testing its impact in one area before expanding, tend to see better results. Lastly, leveraging AI within communities of professionals who have experience in sales operations or incentive compensation can help teams build confidence in AI’s potential.

For companies that are new to AI, what’s an easy first step they can take to get started?

First, check if your organization already has AI tools that are approved for use. Many companies now have AI tools built into their software stack, but employees may not realize they’re available. If you do have access to an AI tool, start small—use it for efficiency tasks like summarizing reports, drafting emails, or troubleshooting common commission questions. If you don’t have access to an enterprise AI tool, experiment with public AI models for non-sensitive tasks to build familiarity with how AI can support your workflow.

As AI continues to advance, how do you see roles in sales operations and incentive compensation evolving?

AI is going to make routine tasks faster, but that doesn’t mean people in these roles will become obsolete. Instead, professionals in sales ops and comp management will shift toward higher-level strategic thinking. AI will handle the repetitive calculations, while admins and leaders will focus more on optimizing comp structures, coaching sales teams, and driving business strategy. There’s also an opportunity for compensation leaders to play a key role in AI adoption—by designing incentive plans that encourage employees to use AI productively, they can help their organizations maximize its potential.

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If you’re interested in participating in one of the Multiplier Q&A features or have questions about AI’s impact on sales performance and incentive compensation, reach out to us at multiplier@captivateiq.com.